Searches for motifs within proteins that are likely to be phosphorylated by specific protein kinases or bind to domains. Scansite is an application to predict short linear sequence motif sites. It uses position-specific scoring matrices (PSSMs) to predict interaction sites that are important in cellular signaling. This application can also be used to show all potential sites in a given protein or all proteins in a database that contains sites for one or more motifs.

A manually curated database of short linear motifs (SLiMs). ELM 2016 contains more than 240 different motif classes with over 2700 experimentally validated instances, manually curated from more than 2400 scientific publications. In addition, more data have been made available as individually searchable pages and are downloadable in various formats.

Integrates multiple data sets on 14-3-3-binding phosphoproteins. ANIA also pinpoints candidate 14-3-3-binding phosphosites using predictor algorithms, assisted by our recent discovery that the human 14-3-3-interactome is highly enriched in 2R-ohnologues. 2R-ohnologues are proteins in families of two to four, generated by two rounds of whole genome duplication at the origin of the vertebrate animals. ANIA identifies candidate 'lynchpins', which are 14-3-3-binding phosphosites that are conserved across members of a given 2R-ohnologue protein family. Other features of ANIA include a link to the catalogue of somatic mutations in cancer database to find cancer polymorphisms that map to 14-3-3-binding phosphosites, which would be expected to interfere with 14-3-3 interactions.

A web server that predicts 14-3-3-binding sites by combining predictions from three different classifiers: ANN, PSSM and SVM. Proteins of interest can be queried using single UniProt accession identifiers or as sequences in FASTA format. A table with prediction scores as well as information on the phosphorylation state of the respective Ser/Thr is provided for each queried protein. Alternatively, a file containing up to 100 protein sequences in FASTA format can be uploaded. 14-3-3-Pred then generates comma/tab-separated output results files that can be easily used to further compare predictions, elaborate hypotheses, and prioritise laboratory experiments to validate the sites.